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106 results for “Tsunami”
Landslides from Space - Nuugaatsiaq: landslide-induced tsunami, Greenland (17th June 2017)
<p>On 17th June 2017 the village of Nuugaatsiaq was struck by a large, isolated tsunami. This Tsunami was caused by a landslide on a nearby cliff.<br> <br> The pre-event acquisition is from 2nd June 2017 (Sentinel-2) and the post-event acquisition is from 19th June 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Data used in the manuscript: "Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh"
<p>The data set presented accompanies the study by Laso Bayas et al. (2011) “Influence of coastal vegetation on the 2004 tsunami wave impact in west Aceh”. The data set contains all the observed (not transformed) variables used in the above mentioned study. The accompanying text file describes each of the variables included. A total of 180 transects were employed for the Laso Bayas et al (2011) study. The variables were further standardized and simplified to use them into the statistical models described in the paper.</p>
Calving flux estimated from tsunami waves
<p>The following geophysical field data in July 2015 and July 2016 at Bowdoin Glacier in northwest Greenland is provided in this dataset: </p> <p>(1) Tsunami wave data: Low-pass filtered tsunami waves record in July 2015 and July 2016. The unit is meter. Local time.</p> <p>(2) UAV ortho-images and DEMs: Tiff format ortho-images and DEMs. Coordinates are in UTM19 north / WGS84.</p> <p>(3) Ice surface speed observed by GPS station near the glacier front. Coordinates are WGS 84. UTC time.</p> <p> </p>
Greek Tsunami Impact Database 1800-2020
<p>Τhe Tsunami Impact Database of Greece covers the time period from AD 1800 to 2020. Sixteen tsunami events have been inserted so far in the database for the reference period. Most of the tsunami events were caused by earthquakes and only two have been triggered by gravitative marine slides.</p><p>For each tsunami event the database provides (1) Parameters of the Tsunami Sources, e.g. earthquakes or landslides, and (2) Tsunami Impact Data. Data for the sections (1) and (2) have been taken from several catalogues, papers, books, technical reports and other publications (see reviews, literature compilations or original observational data in Galanopoulos, 1957, 1960, Papadopoulos, 2003, Ambraseys, 1960, Papadopoulos et al., 2007, 2010, Dogan et al., 2019, 2021, Triantafyllou et al., 2021). </p><p>The Parameters of the Earthquake Tsunami Sources include earthquake origin time, epicentral coordinates, focal depth, moment magnitude Mw, and earthquake maximum intensity Io. For the tsunamigenic earthquakes that occurred in the time interval from AD 1800 to 1899 source parameters have been taken from the SHEEC (Stucchi et al., 2013). For the post-1900 time period, earthquake source parameters have been adopted from the ISC-GEM (2023) catalogue or from the GCMT project (https://www.globalcmt.org/) for the post- 1975 time interval. The Parameters of the Landslide Tsunami Sources include date and geographical coordinates. </p><p>For all sources the database include also the region and sub-region as well as the next categorization of the seismic source by following the categorization adopted within the pan-European tsunami projects GITEC (https://cordis.europa.eu/project/id/EV5V0175), GITEC-TWO (https://cordis.europa.eu/project/id/ENV4960297) and TRANSFER (https://cordis. europa.eu/project/id/37058): ER=earthquake, EL= earthquake landslide, ES= earthquake marine slide, GS= gravitative marine slide. A short description of each event is also provided and a reliability score, Rel, for the tsunami event is given in a 4-degree scale (for further explanations οn how this scale is arranged see in other publications, e.g. Papadopoulos, 2003). </p><p>Depending on the data availability an effort has been made to introduce quantitative impact data to the extent it is possible (e.g., numbers of fatalities and injuries, numbers of buildings or vessels damaged, etc.). From the impact of a tsunami event the maximum tsunami intensity, K, has been estimated according to the 12-grade scale of Papadopoulos and Imamura (2001). For each tsunami event corresponding references (Ref) are also provided.</p>
Tsunami Simulation: TsunAWI Testcase "Coquimbo 2015", Setups and Example Results
<p>This dataset contains three setups with example results for TsunAWI simulations for the Chilean coast with a focus on<br> the Coquimbo region. They are used in the LEXIS project, WP6 "Earthquake and Tsunami Pilot".</p> <p><strong>coquimbo_150m.tgz </strong>- coarse setup with down to 150m resolution in Coquimbo and other areas of interest along the Chilean coast</p> <p><strong>coquimbo_20m.tgz </strong>- fine<strong> </strong>setup with down to 20m resolution in Coquimbo, also high resolution along other areas of interest along the Chilean coast</p> <p><strong>coquimbo_20m_new.tgz </strong>- setup tuned for the LEXIS workflow aiming at a real time estimate of the inundation on the City of Coquimbo, thus a focus with 20m resolution only in Coquimbo region.</p> <p> </p> <p>Each dataset includes a README explaining the included files. The following description is taken as pars pro toto from coquimbo_20m_new.tgz.</p> <p><strong>Computational Mesh</strong></p> <ul> <li>mesh/Coquimbo_20m/nod2D.out <br> Location of mesh vertices in geographical coordinates, vertices on the open boundary are flagged with "1"<br> Resolution in Coquimbo area: 20m</li> <li>mesh/Coquimbo_20m/nodhn.out<br> Water depth at the respective vertices. Negative values denote elevation on land. <br> In the ocean: ‘The GEBCO_08 Grid, version 20090202 (http://www.gebco.net)’ <br> On land: SRTM, Shuttle radar topography mission, 1 arcsec resolution, https://www2.jpl.nasa.gov/srtm/ Bilinear interpolation to the mesh vertices.</li> <li>mesh/Coquimbo_20m/elem2D.out<br> Mesh geometry: List of the vertices forming the triangular elements.</li> </ul> <p><strong>Input Parameters</strong></p> <ul> <li>namelist.tsunami<br> Parameters read by TsunAWI at run time, e.g., <ul> <li>path to the mesh and the output</li> <li>the time step dt and the time frame of the simulation T_end</li> <li>physical parameters like Manning friction, viscosity</li> <li>toggle the output </li> </ul> </li> <li>Additional command line parameters<br> The results below are for an idealized source (Cosine bell at the location of the 2015 Coquimbo event, started with <br> ompTsuna.x -xy -71.674 -31.573 -mw 8.3 -id coquimbo_20m</li> </ul> <p><strong>Result</strong></p> <ul> <li>coquimbo_20m_new.nc <br> Example output in netcdf format, raw data on the triangular mesh, see<br> ncdump -h coquimbo_20m.nc </li> <li>coquimbo_20m_new_inundation.nc<br> The same raw data but restricted to the inundated area, for faster postprocessing.</li> </ul> <p>The TsunAWI version used is <br> https://gitlab.awi.de/tsunawi/tsunawi/-/tags/LEXIS<br> with one minor bugfix: Add <br> use mpi_parallel<br> to line 881 of tsunawi/src/parameters.F90 </p> <p>Compiled with tsunawi/src/Makefile.in_karolina<br> and run on karolina.it4i.cz in MPI-parallel mode with 128 tasks.</p> <p> </p> <p>This work was supported by the LEXIS project funded by the EU’s Horizon 2020 research and innovation programme (2014-2020) under grant agreement No 825532.</p>
Traveling Waves: The Indonesian Tsunami Warning System - STS Webinar Series #1
<p>Previously published on YouTube: https://www.youtube.com/watch?v=CWK1_Nxi__Y</p> <p>(Indonesian)</p> <p>Seri webinar ini diadakan oleh Pusat Riset Kewilayahan (PRW) BRIN untuk mendiskusikan tema-tema terkait Science, Technology, and Society Studies, salah satu program kajian yang dikembangkan di PRW-BRIN. Webinar ini membahas kelindan antara diskursus, aktor, mesin deteksi, serta praktik sains dan teknologi untuk menghadapi risiko dan ketidakpastian sistem pengetahuan peringatan dini tsunami di Indonesia. Dengan menggunakan pendekatan etnografis, diskusi ini akan berusaha menjawab pertanyaan terkait teknologi sistem peringatan dini tsunami Indonesia sebagai infrastruktur sosial dan jaringan konektivitas pakar Jerman - Indonesia dalam pengembangan sistem teknologi tersebut di Indonesia.</p> <p>(English)</p> <p>This webinar series was held by the BRIN Regional Research Center (PRW) to discuss themes related to Science, Technology, and Society Studies, one of the study programs developed at PRW-BRIN. This webinar discusses the interplay between discourse, actors, detection machines, as well as science and technology practices to deal with the risks and uncertainties of the tsunami early warning knowledge system in Indonesia. Using an ethnographic approach, this discussion will attempt to answer questions related to the technology of the Indonesian tsunami early warning system as a social infrastructure and connectivity network of German-Indonesian experts in developing the technology system in Indonesia.</p>
Global amplification factors for tsunami inundation
<p><strong>Global tsunami amplification factors used for estimation of tsunami run-up, based on wave input from offshore mariograms.</strong></p> <p>The off-shore maximum surface elevation is multiplied by the factor to achieve the MIH estimation. Two sets of factors are included, one for the Mediterranean Sea (Glimsdal et al., 2019) and one set of global factors (Løvholt et al., 2012). The factors depends on the wave period, polarity and coastal location.</p> <p>The primary objective of this service will be to give scientists a tool for regional tsunami hazard evaluations, based on time series extracted by tsunami propagation models, without running computational costly run-up models along the coastlines. Example on global evaluation of MIH is found in Figure 2.</p> <p>The model will provide the following features:</p> <ul> <li>Interface for extraction of the wave characteristics from off-shore mariograms (time history of the surface elevation at a gauge)</li> <li>Extract the amplification factors based on the wave characteristics</li> <li>Calculation of the maximum inundation height</li> </ul> <p>Examples on Python-codes that can be used to calculate factors for different types of input will be added soon.</p> <p>The method is described in the following open access publications:</p> <p><a href="https://link.springer.com/article/10.1007%2Fs00024-019-02091-w">https://link.springer.com/article/10.1007%2Fs00024-019-02091-w</a></p> <p>This service is part of the <strong><a href="https://tsunamidata.org/">candidate Thematic Core Service for tsunami</a></strong> and will be integrated into the <a href="https://www.ics-c.epos-eu.org/"><strong>Integrated Core Service Data </strong></a>Portal of the <a href="https://www.epos-eu.org/"><strong>European Plate Observing System (EPOS)</strong>.</a></p>
Final data of the adjoint-state full waveform tsunami source inversion, applied to Chile-Iquique tsunami event
<p>We develop an adjoint-state full waveform inversion procedure to recover the initial water elevation of a tsunami event. Traditional finite-fault tsunami source inversion methods suffer from the uncertainty of fault parameters or crustal rigidity. Moreover, the heavy computational burden of calculating Green’s functions results in limited spatial resolution and hinders the real-time applicability of the traditional methods to tsunami early warning. In this work, we apply the adjoint-state full waveform inversion method to the tsunami source inversion. The benefits of the adjoint inversion are two folds: 1) independence of fault parameters, and 2) high computational efficiency, especially for dense tsunami arrays and high resolution grids. We valid this approach with synthetic tsunami sources, and apply it to the 2014 Chile-Iquique tsunami event. Both synthetic and real-data preliminary results show that the adjoint-state method is of high efficiency and high resolution, outperforming the traditional tsunami source inversions. </p> <p>The data is in three comma-separated ascii files. We shared the three inversion results with different starting models. The source region is 70.3~71.5W, 18.5~21S on uniform grids. The src_TRIstart.txt is the inversion result with TRI image starting model, src_USGS_unistart.txt is the inversion result with USGS uniform slip model (https://earthquake.usgs.gov/earthquakes/eventpage/usc000nzvd/finite-fault). The src_zerostart.txt is the inversion result with zero starting model. The text file has longitude (in degrees), latitude (in degrees) and water elevation (in meters) of each column.</p>
Dataset for the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings"
<p>The results of the analysis in the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings" published in Geophysical Research Letters are available here. For the details of the file, please see Readme.pdf.</p>
Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Saip tide gauge station at 10:08 hours CET
<p>GoogleEarth-Screenshot of Saip tide gauge station on March 11 at 10:08 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).</p>
Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Wake tide gauge station at 10:07 hours CET
<p>GoogleEarth-Screenshot of Wake tide gauge station on March 11 at 10:07 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).</p>
Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Hansaki tide gauge station at 09:56 hours CET
<p>GoogleEarth-Screenshot of Hanasaki tide gauge station on March 11 at 09:56 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).</p>
Tsunamis due to ice masses: Different calving mechanisms and linkage to landslide-tsunamis - Dataset
<p>Land ice melt and retreat is one of the most visible effects of climate change and contributes ≈1.5 mm/year to global sea-level rise (SLR) of a total of ≈2.7 mm/year. Global warming results in the shrinking of ice masses in most ice covered regions in the World, particularly in the Alps and in Greenland and the Greenlandic mass loss is estimated at –269 ±51 Gt/year. A significant part of this mass loss is through the detachment of icebergs at glacier fronts in a mechanism called iceberg calving. Such iceberg impacting into a water body generate tsunamis, such called "iceberg-tsunamis". Such an iceberg-tsunami reached a height of 50 m at the Eqip Sermia outlet glacier in 2014. These tsunamis pose a considerable hazard for the local community, the fishing industry and the increasing number of tourists in ice covered areas. Several iceberg calving mechanisms have been proposed including fall, over-turning and capsizing. Reliable guidance on the upper limit of iceberg-tsunami heights are currently unavailable. A main reason for this limited understanding is that reliable field data are rare, such that laboratory tests complemented with numerical simulations are important to advance this research field. This was the aim of this HYDRALAB+ funded study. The wave features (height, length, velocity) caused by icebergs in function of the iceberg calving mechanisms (fall, over-turning, capsizing), as well as the mass volume and kinematics, were modelled in unique large-scale experiments. This minimised both scale effects and wave reflection. The attached files and folders include information about and data from these experiments.</p>
Tsunamis due to ice masses: Different calving mechanisms and linkage to landslide-tsunamis - Data storage report
<p>Land ice melt and retreat is one of the most visible effects of climate change and contributes ≈1.5 mm/year to global sea-level rise (SLR) of a total of ≈2.7 mm/year. Global warming results in the shrinking of ice masses in most ice covered regions in the World, particularly in the Alps and in Greenland and the Greenlandic mass loss is estimated at –269 ±51 Gt/year. A significant part of this mass loss is through the detachment of icebergs at glacier fronts in a mechanism called iceberg calving. Such iceberg impacting into a water body generate tsunamis, such called "iceberg-tsunamis". Such an iceberg-tsunami reached a height of 50 m at the Eqip Sermia outlet glacier in 2014. These tsunamis pose a considerable hazard for the local community, the fishing industry and the increasing number of tourists in ice covered areas. Several iceberg calving mechanisms have been proposed including fall, over-turning and capsizing. Reliable guidance on the upper limit of iceberg-tsunami heights are currently unavailable. A main reason for this limited understanding is that reliable field data are rare, such that laboratory tests complemented with numerical simulations are important to advance this research field. This was the aim of this HYDRALAB+ funded study. The wave features (height, length, velocity) caused by icebergs in function of the iceberg calving mechanisms (fall, over-turning, capsizing), as well as the mass volume and kinematics, were modelled in unique large-scale experiments. This minimised both scale effects and wave reflection. The attached file is an HYDRALAB+ standard Data Storage Report about these experiments.</p>
Figure 1 in STEER - SUNDA STRAIT TSUNAMI (INDONESIA): PRELIMINARY VIRTUAL ASSESSMENT TEAM (P-VAT) REPORT
Figure 1. – Preserved specimens of Phenacoscorpius megalops from Malo Island, Vanuatu (MHMN-IC-2012-0833). A: 81.7 mm SL; B: 57.8 mm SL.
Figure 2 in STEER - SUNDA STRAIT TSUNAMI (INDONESIA): PRELIMINARY VIRTUAL ASSESSMENT TEAM (P-VAT) REPORT
Figure 2. – Distribution records of Phenacoscorpius megalops. Stars and circle indicate the previous locality records of the species and of the specimens report- ed in this study, respectively.
Figure 7 in Effect of environmental change after the 2011 tsunami on the population dynamics of Japanese tubesnout Aulichthys japonicus (Gasterosteiformes)
Figure 7. – Photographs showing scraped an ascidian Halocynthia roretzi (A) and an egg mass (B) of the Japanese tubesnout Aulichthys japonicus, which was spawned and developed within the ascidians.
Figure 4 in Effect of environmental change after the 2011 tsunami on the population dynamics of Japanese tubesnout Aulichthys japonicus (Gasterosteiformes)
Figure 4. – Relationship between the occurrence of larvae of Aulichthys japonicus collected from 2012 to 2013 and water temperature (°C).
Figure 3 in Effect of environmental change after the 2011 tsunami on the population dynamics of Japanese tubesnout Aulichthys japonicus (Gasterosteiformes)
Figure 3. – Monthly changes in the number of individuals for larva, juvenile and young Aulichthys japonicus and water temperature collected at the seagrass bed in Namiita beach in 2012.
Figure 6 in Effect of environmental change after the 2011 tsunami on the population dynamics of Japanese tubesnout Aulichthys japonicus (Gasterosteiformes)
Figure 6. – Changes in the surface of the seagrass bed area (m2) since before Tsunami (according to Yamaki, 2009) to 2014.
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International Brain Laboratory public data
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OpenNeuro
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